# Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review

arXiv: https://arxiv.org/abs/2608.19214 · HTML: https://mlsanity.com/paper/2608.19214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 175 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Distributionally Robust Stochastic Optimization with Wasserstein Distance](https://mlsanity.com/paper/1604.02199/) (2016)
- [Biased Random-Key Genetic Algorithms: A Review](https://mlsanity.com/paper/2312.00961/) (2023)
- [Chance-Constrained Optimization under Limited Distributional Information: A Review of Reformulations Based on Sampling and Distributional Robustness](https://mlsanity.com/paper/2101.08746/) (2021)
- [Early years of Biased Random-Key Genetic Algorithms: A systematic review](https://mlsanity.com/paper/2405.01765/) (2024)
- [Distributionally Robust Optimization with Moment Ambiguity Sets](https://mlsanity.com/paper/2103.12315/) (2021)
- [Risk-Adaptive Approaches to Stochastic Optimization: A Survey](https://mlsanity.com/paper/2212.00856/) (2022)
- [Recoverable Robust Single Machine Scheduling with Polyhedral Uncertainty](https://mlsanity.com/paper/2011.06284/) (2020)
- [Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging](https://mlsanity.com/paper/2308.08522/) (2023)

## Abstract (arXiv)

The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportation and freight logistics, where vessel arrivals, berth positions, service durations, and quay?crane availability are tightly coupled. Under uncertainties such as arrival deviations, handling-time fluctuations, and resource disruptions, schedules optimized under nominal assumptions may become fragile during execution, motivating the study of robust metaheuristic optimization for BACAP in port-terminal operations. Although population-based metaheuristics have been widely used for BACAP and related port-scheduling problems, existing studies remain fragmented in their uncertainty repre?sentations, robustness criteria, search mechanisms, and empir?ical evaluation protocols. To the best of our knowledge, this paper provides the first focused review dedicated to robust population-based metaheuristics for BACAP under uncertainty. We first summarize uncertainty sources and information repre?sentations in BACAP, and then organize existing methods from a mechanism-oriented perspective, covering solution representation and decoding, robust evaluation and selection, robustness-guided search dynamics, and feasibility preservation and recovery. We further present a benchmark suite for uncertain BACAP to support controlled empirical comparison and report illustrative baseline results by combining representative metaheuristics with different robustness strategies. Finally, we identify open chal?lenges related to benchmark extension, robustness-aware search design, time-adaptive robustness, and non-stationary uncertainty.